Independent journalists, human rights documentarians, and at-risk publishers overwhelmingly operate on shared-host WordPress installations nfrastructure they do not control and cannot audit. Existing tamper-evident publishing solutions require dedicated servers, compiled native libraries, or HSM-backed key storage, placing them beyond reach of the population most exposed to infrastructure-level content suppression. We present ArchivioMD, a cryptographic content integrity system deployable on shared-host PHP 7.4+ without native extensions, root access, or Composer dependencies. ArchivioMD combines four composable integrity layers: Deterministic content […]

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arXiv:2602.09120v1 Announce Type: new Abstract: Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures. Small variations in solution or operating conditions can shift the jet regime, generating non Gaussian fiber diameter distributions. Despite substantial progress, no existing framework enables inverse design toward desired fiber outcomes while integrating polymer solvent chemical constraints or predicting full distributions. SpinCastML is an open source, distribution aware, chemically informed machine learning and Inverse Monte Carlo (IMC) software […]

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Claude’s new constitution Late last year Richard Weiss found something interesting while poking around with the just-released Claude Opus 4.5: he was able to talk the model into regurgitating a document which was not part of the system prompt but appeared instead to be baked in during training, and which described Claude’s core values at great length. He called this leak the soul document, and Amanda Askell from Anthropic quickly confirmed that it was indeed part of Claude’s […]

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arXiv:2601.07979v1 Announce Type: cross Abstract: Although extensive research exists in spatial modeling, few studies have addressed finite mixture model-based clustering methods for spatial data. Finite mixture models, especially Gaussian mixture models, particularly suffer from high dimensionality due to the number of free covariance parameters. This study introduces a spatial covariance constraint for Gaussian mixture models that requires only four free parameters for each component, independent of dimensionality. Using a coordinate system, the spatially constrained Gaussian mixture model enables […]

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arXiv:2602.03986v1 Announce Type: new Abstract: We study the effect of group symmetrization of pre-trained models on conformal prediction (CP), a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under the assumption of data exchangeability. Unfortunately, CP uncertainty regions can grow significantly in long horizon missions, rendering the statistical guarantees uninformative. To that end, we propose infusing CP with geometric information via group-averaging of the pretrained predictor to distribute the non-conformity mass across the orbits. […]

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arXiv:2602.05012v1 Announce Type: new Abstract: In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks with only a small set of examples at inference time, thereby avoiding task-specific fine-tuning. However, in-context examples may contain privacy-sensitive information that should not be revealed through model outputs. Existing differential privacy (DP) approaches to ICL are either computationally expensive or rely on heuristics with limited effectiveness, including context oversampling, synthetic data generation, or unnecessary thresholding. We reformulate private ICL […]

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This paper establishes a rigorous theoretical foundation for the function class implicitly learned by XGBoost, bridging the gap between its empirical success and our theoretical understanding. We introduce an infinite-dimensional function class $mathcal{F}^{d, s}_{infty-text{ST}}$ that extends finite ensembles of bounded-depth regression trees, together with a complexity measure $V^{d, s}_{infty-text{XGB}}(cdot)$ that generalizes the $L^1$ regularization penalty used in XGBoost. We show that every optimizer of the XGBoost objective is also an optimizer of an equivalent penalized regression problem over […]

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arXiv:2601.19928v1 Announce Type: new Abstract: Reinforcement learning (RL) has catalyzed the emergence of Large Reasoning Models (LRMs) that have pushed reasoning capabilities to new heights. While their performance has garnered significant excitement, exploring the internal mechanisms driving these behaviors has become an equally critical research frontier. This paper provides a comprehensive survey of the mechanistic understanding of LRMs, organizing recent findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. By synthesizing these […]

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arXiv:2601.06730v2 Announce Type: replace-cross Abstract: The Rashomon effect — the existence of multiple, distinct models that achieve nearly equivalent predictive performance — has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effect, organizing them into three categories: statistical sources arising from finite samples and noise in the data-generating process; structural sources arising from non-convexity of optimization objectives and unobserved variables that create fundamental non-identifiability; and […]

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arXiv:2604.14210v1 Announce Type: new Abstract: A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, potentially reducing costs by up to 40%. This claim has influenced developers to consider switching to Chinese for “vibe coding” to save on API costs. In this paper, we conduct a rigorous empirical study using SWE-bench Lite, a benchmark of software engineering tasks, to evaluate whether this claim of Chinese […]

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